UCSF - Annotation

Load the required packages

library(Seurat)
library(SingleR)
library(celldex)
library(SeuratDisk)
library(clustree)

Load the dataset

PS: The hdf5 file was loaded on scanpy and the files were saved in this format for ease of use. (code attached in the email)

matrix_path <- "./seurat_conversion/counts.mtx"
features_path <- "./seurat_conversion/features.tsv"
barcodes_path <- "./seurat_conversion/barcodes.tsv"

# Read in the matrix, features, and barcodes as a Seurat object
obj <- CreateSeuratObject(counts = ReadMtx(mtx = matrix_path,features = features_path, cells = barcodes_path, feature.column = 1  ), meta.data = read.csv("metadata.csv", row.names = 1))

## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')
## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')

obj[["percent.mt"]] <- PercentageFeatureSet(obj, pattern = "^MT-")
FeatureScatter(obj, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")

Pre-processing

#Pre-process 
obj <- NormalizeData(obj)
obj <- FindVariableFeatures(obj)
obj <- ScaleData(obj)

## Centering and scaling data matrix

Dimensionality reduction

#PCA
obj <- RunPCA(obj, features = VariableFeatures(object = obj))

## PC_ 1 
## Positive:  CD37, CORO1A, ARHGDIB, PTPRC, LSP1, LAPTM5, RAC2, LCP1, CD74, HLA-DRA 
##     ITGB2, HLA-B, HCST, SRGN, HLA-DRB1, CD52, HLA-DPA1, TAGAP, TYROBP, RCSD1 
##     ALOX5AP, FCER1G, CD69, NCF1, AIF1, LTB, SPI1, LST1, HLA-DPB1, DUSP2 
## Negative:  MDK, NFIB, CALD1, SPARC, TPM1, RBP1, MEG3, COL1A2, CRIP2, FN1 
##     ID1, COL3A1, COL1A1, COL6A2, BEX3, IGFBP5, MMP2, PTN, MFAP2, COL4A2 
##     MYL9, MAP1B, CPE, FBLN1, CKB, TPM2, CLU, ELN, COL5A2, KRT8 
## PC_ 2 
## Positive:  AGR3, C1orf194, TSPAN1, C20orf85, FAM183A, SMIM22, C9orf24, CAPS, FXYD3, CCDC78 
##     CAPSL, ZMYND10, MORN5, FAM92B, RSPH1, PIFO, SNTN, C5orf49, C11orf88, FAM216B 
##     TEKT1, DYNLRB2, LRRC46, DNAAF1, WDR38, TMEM190, DNAH5, DNAH12, SPAG6, C22orf15 
## Negative:  VIM, CALD1, RACK1, FN1, COL5A2, AKAP12, BEX3, COL6A2, TPM2, COL4A2 
##     COL4A1, COL3A1, GPC6, MMP2, NR2F1, ZEB2, CDH11, PRKG1, UNC5C, SPARC 
##     ADGRB3, RBP1, COL1A2, LSAMP, COL6A3, PTPRG, ADGRL3, ROR1, ZFPM2, POSTN 
## PC_ 3 
## Positive:  GNB2L1, SEPP1, GLTSCR2, NGFRAP1, ATP5G2, ATP5L, ATP5E, S100A11, C14orf2, ATP5I 
##     LGALS1, TCEB2, TM4SF1, UQCR11.1, ATP5G3, ELN, JUNB, B2M, IFITM3, ATP5B 
##     ATP5J2, MGP, CAV1, USMG5, PTRF, IGF2, A2M, IGFBP4, ZFP36, SEPW1 
## Negative:  ELAVL4, LINC00682, STMN2, CD24, INA, CHRNA3, KIF21A, PHOX2B, PHOX2A, STMN4 
##     SCG2, NEFM, PRPH, BEX1, GAP43, NEFL, EML5, PCSK1N, HAND2, CTNNA2 
##     ANK2, UCHL1, SCG3, ASCL1, CRABP1, HAND2-AS1, TUBB2B, EEF1A2, TAC3, NNAT 
## PC_ 4 
## Positive:  MNDA, CSF3R, S100A8, FCN1, C5AR1, FPR1, S100A9, LYZ, RETN, PLAUR 
##     S100A12, BCL2A1, CXCL8, IL1B, SERPINA1, CD14, SMIM25, TYROBP, CD68, AQP9 
##     FGL2, FCER1G, CYBB, CDA, CSF1R, MS4A6A, IL1RN, MCEMP1, CTSS, IFI30 
## Negative:  PTPRCAP, CD79A, CD79B, VPREB3, TCL1A, IGHM, FAM129C, MZB1, GNB2L1, GLTSCR2 
##     CD69, ATP5E, ATP5L, ATP5G2, IGLL5, C14orf2, FCRLA, ATP5I, ATP5G3, TCEB2 
##     UQCR11.1, LTB, CD19, ATP5B, FCMR, ATP5J2, USMG5, NGFRAP1, CD38, CD72 
## PC_ 5 
## Positive:  ELAVL4, STMN2, CHRNA3, LINC00682, INA, PRPH, STMN4, PHOX2B, PHOX2A, SCG2 
##     BEX1, RTN1, GAP43, NEFL, WFDC2, CRABP1, HAND2, NEFM, GNB2L1, EML5 
##     ASCL1, EEF1A2, CSTA, SCGB3A1, GLTSCR2, HAND2-AS1, ATP5G3, HBM, AHSP, S100A9 
## Negative:  C11orf88, CAPSL, FAM216B, FAM92B, SNTN, TEKT1, C11orf97, MORN5, WDR38, C22orf15 
##     CFAP126, DNAH9, LRRC46, DNAH12, EFCAB1, ZMYND10, TUBA4B, ROPN1L, FAM183A, AL357093.2 
##     SPAG6, DYNLRB2, CAVIN1, C5orf49, CFAP53, C20orf85, CFAP157, TEKT2, AC007906.2, CAV1

ElbowPlot(obj) #Knee at 20 PC

DimHeatmap(obj, dims = 1:10, cells = 500, balanced = TRUE) #Suggests likely existence of batch effect - look for biological reasons for variations

Idents(obj) <- "platform"
DimPlot(obj, reduction = "pca")

### Clustering of un-integrated object

#Clustering
obj <- FindNeighbors(obj, dims = 1:20)

## Computing nearest neighbor graph

## Computing SNN

obj <- FindClusters(obj, resolution = c(0.2, 0.3, 0.4, 0.5, 0.7, 0.8))

## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 21486
## Number of edges: 723161
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9810
## Number of communities: 31
## Elapsed time: 1 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 21486
## Number of edges: 723161
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9761
## Number of communities: 36
## Elapsed time: 1 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 21486
## Number of edges: 723161
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9722
## Number of communities: 39
## Elapsed time: 1 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 21486
## Number of edges: 723161
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9685
## Number of communities: 43
## Elapsed time: 1 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 21486
## Number of edges: 723161
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9622
## Number of communities: 47
## Elapsed time: 1 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 21486
## Number of edges: 723161
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9593
## Number of communities: 51
## Elapsed time: 1 seconds

Validate the clustering

clustree::clustree(obj, prefix = "RNA_snn_res.")

Idents(obj) <- "RNA_snn_res.0.4" #least crossover

Non-linear dimensionality reduction

#UMAP
obj <- RunUMAP(obj, dims = 1:20)

## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session

## 19:40:46 UMAP embedding parameters a = 0.9922 b = 1.112

## 19:40:47 Read 21486 rows and found 20 numeric columns

## 19:40:47 Using Annoy for neighbor search, n_neighbors = 30

## 19:40:47 Building Annoy index with metric = cosine, n_trees = 50

## 0%   10   20   30   40   50   60   70   80   90   100%

## [----|----|----|----|----|----|----|----|----|----|

## **************************************************|
## 19:40:48 Writing NN index file to temp file C:\Users\MEENAK~1\AppData\Local\Temp\RtmpqsFoIK\file47187fd05842
## 19:40:48 Searching Annoy index using 1 thread, search_k = 3000
## 19:40:52 Annoy recall = 100%
## 19:40:52 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 19:40:54 Initializing from normalized Laplacian + noise (using irlba)
## 19:40:55 Commencing optimization for 200 epochs, with 889856 positive edges
## 19:41:11 Optimization finished

DimPlot(obj, reduction = "umap", label = TRUE)

DimPlot(obj, reduction = "umap", group.by = "developmental_stage", label = TRUE) #Likely cause for batch effect due to biological differences

DimPlot(obj, reduction = "umap", group.by = "site", label = TRUE) #Likely cause for batch effect due to biological differences

DimPlot(obj, reduction = "umap", group.by = "platform", label = TRUE) #Some technical variation exists

As seen in the above UMAP plots, the batch effect can exist dueto the different stages in developement or can be of technical origin. Correcting for batch effects wrt technical variable - platforms.

Batch Effect Correction wrt Platform

#Batch effect corrections wrt platform
obj.list <- SplitObject(obj, split.by = "platform")
obj.list <- lapply(X = obj.list, FUN = function(x) {
  x <- NormalizeData(x)
  x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 5000)
})

features <- SelectIntegrationFeatures(object.list = obj.list)
anchors <- FindIntegrationAnchors(object.list = obj.list, anchor.features = features)

## Scaling features for provided objects

## Finding all pairwise anchors

## Warning: Layer counts isn't present in the assay object; returning NULL
## Warning: Layer counts isn't present in the assay object; returning NULL

## Running CCA

## Merging objects

## Finding neighborhoods

## Finding anchors

##  Found 8700 anchors

## Filtering anchors

##  Retained 6500 anchors

## Warning: Layer counts isn't present in the assay object; returning NULL
## Warning: Layer counts isn't present in the assay object; returning NULL

## Running CCA

## Merging objects

## Finding neighborhoods

## Finding anchors

##  Found 5888 anchors

## Filtering anchors

##  Retained 4572 anchors

## Warning: Layer counts isn't present in the assay object; returning NULL
## Warning: Layer counts isn't present in the assay object; returning NULL

## Running CCA

## Merging objects

## Finding neighborhoods

## Finding anchors

##  Found 6097 anchors

## Filtering anchors

##  Retained 5198 anchors

combined <- IntegrateData(anchorset = anchors)

## Warning: Layer counts isn't present in the assay object; returning NULL
## Warning: Layer counts isn't present in the assay object; returning NULL

## Merging dataset 3 into 2

## Extracting anchors for merged samples

## Finding integration vectors

## Finding integration vector weights

## Integrating data

## Warning: Layer counts isn't present in the assay object; returning NULL
## Warning: Layer counts isn't present in the assay object; returning NULL
## Warning: Layer counts isn't present in the assay object; returning NULL

## Merging dataset 1 into 2 3

## Extracting anchors for merged samples

## Finding integration vectors

## Finding integration vector weights

## Integrating data

## Warning: Layer counts isn't present in the assay object; returning NULL
## Warning: Layer counts isn't present in the assay object; returning NULL
## Warning: Layer counts isn't present in the assay object; returning NULL

DefaultAssay(combined) <- "integrated"

combined <- ScaleData(combined, verbose = FALSE)
combined <- RunPCA(combined, npcs = 30, verbose = FALSE)
combined <- RunUMAP(combined, reduction = "pca", dims = 1:30)

## 19:46:36 UMAP embedding parameters a = 0.9922 b = 1.112

## 19:46:36 Read 21486 rows and found 30 numeric columns

## 19:46:36 Using Annoy for neighbor search, n_neighbors = 30

## 19:46:36 Building Annoy index with metric = cosine, n_trees = 50

## 0%   10   20   30   40   50   60   70   80   90   100%

## [----|----|----|----|----|----|----|----|----|----|

## **************************************************|
## 19:46:37 Writing NN index file to temp file C:\Users\MEENAK~1\AppData\Local\Temp\RtmpqsFoIK\file471843c7b1f
## 19:46:37 Searching Annoy index using 1 thread, search_k = 3000
## 19:46:41 Annoy recall = 100%
## 19:46:41 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 19:46:43 Initializing from normalized Laplacian + noise (using irlba)
## 19:46:44 Commencing optimization for 200 epochs, with 916816 positive edges
## 19:47:00 Optimization finished

combined <- FindNeighbors(combined, reduction = "pca", dims = 1:30)

## Computing nearest neighbor graph
## Computing SNN

combined <- FindClusters(combined, resolution = c(0.2, 0.3, 0.4, 0.5, 0.7, 0.8))

## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 21486
## Number of edges: 781467
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9754
## Number of communities: 21
## Elapsed time: 1 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 21486
## Number of edges: 781467
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9699
## Number of communities: 27
## Elapsed time: 1 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 21486
## Number of edges: 781467
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9654
## Number of communities: 30
## Elapsed time: 1 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 21486
## Number of edges: 781467
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9615
## Number of communities: 31
## Elapsed time: 1 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 21486
## Number of edges: 781467
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9543
## Number of communities: 35
## Elapsed time: 1 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 21486
## Number of edges: 781467
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9511
## Number of communities: 37
## Elapsed time: 1 seconds

DimPlot(combined, reduction = "umap", label = TRUE, repel = TRUE)

DimPlot(combined, reduction = "umap", group.by = "platform")

DimPlot(combined, reduction = "umap", group.by = "developmental_stage")

DimPlot(combined, reduction = "umap", group.by = "site", label = TRUE)

Check for any clusters with high levels of mitochondrial gene expression

FeaturePlot(combined, features = "percent.mt") 

Cluster 7 shows presence of a few cells where mitochondrial genes are expressed.

Annotation

The clusters were annotated on the basis of the presence of markers derived from these papers as well as on the basis of cluster markers found using FindAllMarkers() and EnrichR. Avoiding automatic annotation as the dataset requires a reference with embryonic cells for better accuracy.

References: 1. https://pmc.ncbi.nlm.nih.gov/articles/PMC7780123/ 2. https://www.cell.com/cell/fulltext/S0092-8674(22)01415-5

#Annotation - Marker genes
#PS: Refer to papers 1&2 for gene markers in embryonic cells create list from dot plots 
DefaultAssay(combined) <- "RNA"
gene_lists <- list(
  Progenitors = c("CD34", "CD74", "MPO", "PLXNC1", "DEPTO", "SOS1", "TTC27", "PPBP", "LGALS1", "KIT", "LMO4", "CPA3","SLC16A9", "FAM177B", "KIF18B", "KIF15","TMCC2", "HBB"),
  Overall = c("SALL1", "HTRA1", "LILRB5", "CD209", "S100A9", "S100A8", "CD1C", "CLEC10A", "CCSER1", "NEGR1", "CCDC50", "AC023590.1", "BIRC3", "KDM2B", "FAM46C", "XBP1", "VPREB3", "PAX5", "SORCS1", "JMY", "KLRD1", "GNLY", "TENM1", "CD5", "RHCE", "SLC25A21", "CPA3", "IL1RL1", "TMEM40", "PF4", "DEPTO", "PDZD8", "MPO", "PRTN3"),
  Myeloid = c("ORM1", "FCGR3B", "MPO", "RNASE2", "MKI67", "CD14", "S100A12", "RBP7", "CX3CR1", "METTL7", "CLQA", "TREM2", "APOE", 
            "SPP1", "LYVE1", "CD1C", "FCER1A", "CLEC10A", "FCGBP", "CES1", "MTMR11", "FCGR3A", "CD300E", "CCR7", "CCL19", 
            "LAMP3", "MMP9", "GCH1", "GBP2", "TRAF1", "CCL22", "GZMB", "JCHAIN", "CLEC9A", "XCR1", "SIGLEC6", "SPINK2", 
            "CD34", "NPR3", "TESPA1", "IGHM", "PRSS2", "ELANE", "DEFA4", "S100A8", "FCN1", "TPSB2", "GATA2", "MYH10", 
            "GATA1", "CLC", "IL5RA", "CCR3", "IL3RA", "ZNF302", "LCN2", "CAMP", "PF4", "MYL9", "CLCN7", "HIST1H4H"),
  Lymphoid = c("PTPRC", "CCR9", "RAG1", "PTGDR2", "HPGDS", "CCR6", "KIT", "CA2", "SCN1B", "NCAM1", "HPN", "GZMK", "TNFRSF9", 
            "RAMP1", "GZMB", "FCRL6", "MKI67", "FOXP3", "CD4", "CD40LG", "CD8A", "KLRC2", "ZNF683", "IFNG-AS1", "SLC4A10", 
            "CXCR6", "KLRB1", "IL4I1", "NPDC1"),
  Erythroid_Endothelial = c("GDF15", "HMOX1", "HBE1", "GYPA", "HBB", "KLF1", "GATA1", "MKI67", "CPEB4", "XPO7", "UCP2", "PROX1", "STAB2", 
            "AKR1C2", "RNASE4", "SCG3", "OMAD", "SSUH2", "DKK2", "GRIA2", "GJA5", "APOL4", "CA4", "S100A3", "ACKR1", 
            "HDAC9", "PLVAP", "THY1", "CD24", "KIT", "IL7R", "EGLN1"),
  PNS = c("FGFBP2", "MFNG", "KCNIP4", "TM4SF4", "CHODL", "NXPH4", "DPY5", "DPY4", "PCP4", "SV2C", "PENK", "TMEM176B", 
            "CXCL12", "GPNMB", "MICALL2", "APOA1", "MBP", "TYMS", "COL20A1", "ITM2A", "ALDH1A1", "OLFML2A", "HNRNPA1P48", 
            "CYR61"),
  B_Lymphoid = c("CD9", "CD27", "CD334", "DNTT", "IL7R", "TYMS", "IGLL5", "TCL1A", "LTB", "LINC01013", "TNFRSF17", "AL133467.1", 
            "CDHR3", "IGF2.1", "CEBPD", "CCL22", "CCR10", "CD5", "IGHD", "JCHAIN", "BCL7A", "HHEX", "CD38", "VPREB1", "CCND3")
)

DotPlot(combined, assay= 'RNA',features = c("CD4","CCR7", "LEF1", "TCF7", "AQP3", "CD69", "CCR6", "CXCR6", "CCL5", "PRDM1", "FOXP3", "CD8A","GZMK", "GZMB", "GNLY", "PRF1"))

Cluster 3 has Cytotoxic T cells, 18 has NAive CD4+ T cells, 6 may be central memory T cells and 31 has NK cells.

#Plot for identifying Progenitor clusters
DotPlot(combined, features = gene_lists$Progenitors, dot.scale = 8) + RotatedAxis()

## Warning: The following requested variables were not found: DEPTO

Cluster 20 and 8 are Endothelium Progenitors (likely vascular). 19 has Basophil Mast cells.

#Plot for identifying PNS clusters
DotPlot(combined, features = gene_lists$Overall, dot.scale = 8) + RotatedAxis()

## Warning: The following requested variables were not found: DEPTO

#Plot for identifying PNS clusters
DotPlot(combined, features = gene_lists$PNS, dot.scale = 8) + RotatedAxis()

## Warning: The following requested variables were not found: DPY5, DPY4

#Plot for identifying Lymphoid cells
DotPlot(combined, features = gene_lists$Lymphoid, dot.scale = 8) + RotatedAxis()

Immune cell clusters are likely 3, 5, 6, 11, 12, 18, 22, 31, 35. Cluster 6 likely has ILC3 cells.

#Plot for identifying Myeloid cells
DotPlot(combined, features = gene_lists$Myeloid, dot.scale = 8) + RotatedAxis()

## Warning: The following requested variables were not found: METTL7, CLQA

#Plot for identifying B cells
DotPlot(combined, features = gene_lists$B_Lymphoid, dot.scale = 8) + RotatedAxis()

## Warning: The following requested variables were not found: CD334, IGF2.1

#Plot for identifying Erythroid and Endothelial
DotPlot(combined, features = gene_lists$Erythroid_Endothelial, dot.scale = 8) + RotatedAxis()

## Warning: The following requested variables were not found: OMAD

From this plot we can confidently annotate clusters 33 (Primitive Erythrocyte), 14(Definitive Reticulocyte),and 15 (Lymphatic Endothelium) while cluster 20 (Arterial Endothelium) can be annotated with a lower confidence level as the markers are not as highly expressed. Cluster 8 has cells that are a part of Vascular Endothelium.36 is likely Venous Endothelium but the confidence for this annotation is very low as it does not merge close to clusters 20 and 8, and also expresses neural progenitor marker FGFBP2 while not merging with 9 or 0.

All remaining clusters were annotated on the basis of markers, by surveying EnrichR using the top 10 positively enriched marker genes for the clusters. The file has been attached in the email.

Cell Assignment

Idents(combined) <- combined$integrated_snn_res.0.8
new.cluster.ids <- c("PCP4+ Neuron", "DCLK1+ Progenitors", "Clara Progenitors", "Cytotoxic T", "B", "CD1C+ DC", "Central Memory T", "Unknown", "Vascular Endothelium", "Schwann Precursor", "Neural Progenitors", "S100A12+ Classical Monocytes", "B", "Neuronal Progenitors" ,"Definitive Reticulocyte", "Lymphatic Endothelium", "Microglia", "Unknown", "Naive CD4+", "Basophil Mast", "Arterial Endothelium", "Unknown", "DC", "Promylocyte-like", "Epithelium", "Clara Progenitors", "Neuroendocrine", "Neuronal Progenitors", "Megakaryocyte", "DCLK1+ Progenitors", "Neural Progenitors", "NK", "BMEC", "Primitive Erythrocyte","Epithelium", "pDC" ,"Venous Endothelium")
names(new.cluster.ids) <- levels(combined)
combined <- RenameIdents(combined, new.cluster.ids)
DimPlot(combined, reduction = "umap", label = TRUE, pt.size = 0.5) 

sessionInfo()

## R version 4.3.2 (2023-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 11 x64 (build 22631)
## 
## Matrix products: default
## 
## 
## locale:
## [1] LC_COLLATE=English_United Arab Emirates.utf8 
## [2] LC_CTYPE=English_United Arab Emirates.utf8   
## [3] LC_MONETARY=English_United Arab Emirates.utf8
## [4] LC_NUMERIC=C                                 
## [5] LC_TIME=English_United Arab Emirates.utf8    
## 
## time zone: America/New_York
## tzcode source: internal
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] clustree_0.5.1              ggraph_2.2.1               
##  [3] ggplot2_3.5.1               SeuratDisk_0.0.0.9021      
##  [5] celldex_1.10.1              SingleR_2.2.0              
##  [7] SummarizedExperiment_1.30.2 Biobase_2.60.0             
##  [9] GenomicRanges_1.52.1        GenomeInfoDb_1.36.4        
## [11] IRanges_2.34.1              S4Vectors_0.38.2           
## [13] BiocGenerics_0.46.0         MatrixGenerics_1.12.3      
## [15] matrixStats_1.3.0           SeuratObject_5.0.2         
## [17] Seurat_4.4.0               
## 
## loaded via a namespace (and not attached):
##   [1] RcppAnnoy_0.0.22              splines_4.3.0                
##   [3] later_1.3.2                   bitops_1.0-8                 
##   [5] filelock_1.0.3                tibble_3.2.1                 
##   [7] polyclip_1.10-7               lifecycle_1.0.4              
##   [9] hdf5r_1.3.11                  globals_0.16.3               
##  [11] lattice_0.22-6                MASS_7.3-60                  
##  [13] backports_1.5.0               magrittr_2.0.3               
##  [15] plotly_4.10.4                 sass_0.4.9                   
##  [17] rmarkdown_2.28                jquerylib_0.1.4              
##  [19] yaml_2.3.10                   httpuv_1.6.15                
##  [21] sctransform_0.4.1             spam_2.10-0                  
##  [23] sp_2.1-4                      spatstat.sparse_3.1-0        
##  [25] reticulate_1.38.0             cowplot_1.1.3                
##  [27] pbapply_1.7-2                 DBI_1.2.3                    
##  [29] RColorBrewer_1.1-3            abind_1.4-5                  
##  [31] zlibbioc_1.46.0               Rtsne_0.17                   
##  [33] purrr_1.0.2                   RCurl_1.98-1.16              
##  [35] tweenr_2.0.3                  rappdirs_0.3.3               
##  [37] GenomeInfoDbData_1.2.10       ggrepel_0.9.5                
##  [39] irlba_2.3.5.1                 listenv_0.9.1                
##  [41] spatstat.utils_3.1-0          goftest_1.2-3                
##  [43] spatstat.random_3.3-1         fitdistrplus_1.2-1           
##  [45] parallelly_1.38.0             DelayedMatrixStats_1.22.6    
##  [47] leiden_0.4.3.1                codetools_0.2-19             
##  [49] DelayedArray_0.26.7           ggforce_0.4.2                
##  [51] tidyselect_1.2.1              farver_2.1.2                 
##  [53] viridis_0.6.5                 ScaledMatrix_1.8.1           
##  [55] BiocFileCache_2.8.0           spatstat.explore_3.3-2       
##  [57] jsonlite_1.8.8                tidygraph_1.3.1              
##  [59] progressr_0.14.0              ggridges_0.5.6               
##  [61] survival_3.7-0                tools_4.3.0                  
##  [63] ica_1.0-3                     Rcpp_1.0.13                  
##  [65] glue_1.7.0                    gridExtra_2.3                
##  [67] xfun_0.47                     dplyr_1.1.4                  
##  [69] withr_3.0.1                   BiocManager_1.30.25          
##  [71] fastmap_1.2.0                 fansi_1.0.6                  
##  [73] digest_0.6.37                 rsvd_1.0.5                   
##  [75] R6_2.5.1                      mime_0.12                    
##  [77] colorspace_2.1-1              scattermore_1.2              
##  [79] tensor_1.5                    spatstat.data_3.1-2          
##  [81] RSQLite_2.3.7                 utf8_1.2.4                   
##  [83] tidyr_1.3.1                   generics_0.1.3               
##  [85] data.table_1.16.0             graphlayouts_1.1.1           
##  [87] httr_1.4.7                    htmlwidgets_1.6.4            
##  [89] S4Arrays_1.0.6                uwot_0.2.2                   
##  [91] pkgconfig_2.0.3               gtable_0.3.5                 
##  [93] blob_1.2.4                    lmtest_0.9-40                
##  [95] XVector_0.40.0                htmltools_0.5.8.1            
##  [97] dotCall64_1.1-1               scales_1.3.0                 
##  [99] png_0.1-8                     spatstat.univar_3.0-0        
## [101] knitr_1.48                    rstudioapi_0.16.0            
## [103] reshape2_1.4.4                checkmate_2.3.2              
## [105] nlme_3.1-166                  curl_5.2.2                   
## [107] cachem_1.1.0                  zoo_1.8-12                   
## [109] stringr_1.5.1                 BiocVersion_3.17.1           
## [111] KernSmooth_2.23-24            parallel_4.3.0               
## [113] miniUI_0.1.1.1                AnnotationDbi_1.62.2         
## [115] pillar_1.9.0                  grid_4.3.0                   
## [117] vctrs_0.6.5                   RANN_2.6.2                   
## [119] promises_1.3.0                BiocSingular_1.16.0          
## [121] dbplyr_2.5.0                  beachmat_2.16.0              
## [123] xtable_1.8-4                  cluster_2.1.4                
## [125] evaluate_0.24.0               cli_3.6.1                    
## [127] compiler_4.3.0                rlang_1.1.4                  
## [129] crayon_1.5.3                  future.apply_1.11.2          
## [131] labeling_0.4.3                plyr_1.8.9                   
## [133] stringi_1.8.4                 viridisLite_0.4.2            
## [135] deldir_2.0-4                  BiocParallel_1.36.0          
## [137] munsell_0.5.1                 Biostrings_2.68.1            
## [139] lazyeval_0.2.2                spatstat.geom_3.3-2          
## [141] Matrix_1.6-5                  ExperimentHub_2.8.1          
## [143] patchwork_1.2.0               sparseMatrixStats_1.12.2     
## [145] bit64_4.0.5                   future_1.34.0                
## [147] KEGGREST_1.40.1               shiny_1.9.1                  
## [149] highr_0.11                    interactiveDisplayBase_1.38.0
## [151] AnnotationHub_3.8.0           ROCR_1.0-11                  
## [153] igraph_2.0.3                  memoise_2.0.1                
## [155] bslib_0.8.0                   bit_4.0.5